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Smart Agriculture in Nanjing Agricultural University: Celebrating 120th Anniversary

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 15000

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Special Issue Editor

Prof. Dr. Lei Shu

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Guest Editor

College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Interests: agricultural IoT; agricultural big data; agricultural equipment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nanjing Agricultural University was funded in 1902 by the Ministry of Education of China. It is a leading institution in agriculture-related disciplines in China and across the world. NJAU is now home to 66 provincial-, ministerial-, and national-level research platforms, including the National Engineering and Technology Center for IT-based Agriculture.

To celebrate this 120th anniversary of NJAU, Sensors will publish this Special Issue entitled “Smart Agriculture in Nanjing Agricultural University: Celebrating the 120th Anniversary”. This Special Issue will collect high-quality full research articles or comprehensive literature reviews in the broad scope of sensor-based smart agriculture.

Prof. Dr. Lei Shu
Guest Editor

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Keywords

smart agriculture
Internet of Things
monitoring systems

Published Papers (5 papers)

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Research

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18 pages, 9065 KiB

Open AccessArticle

Multi-Objective Association Detection of Farmland Obstacles Based on Information Fusion of Millimeter Wave Radar and Camera

by Pengfei Lv, Bingqing Wang, Feng Cheng and Jinlin Xue

Sensors 2023, 23(1), 230; https://doi.org/10.3390/s23010230 - 26 Dec 2022

Cited by 4 | Viewed by 1967

Abstract

In order to remedy the defects of single sensor in robustness, accuracy, and redundancy of target detection, this paper proposed a method for detecting obstacles in farmland based on the information fusion of a millimeter wave (mmWave) radar and a camera. Combining the advantages of the mmWave radar in range and speed measurement and the camera in type identification and lateral localization, a decision-level fusion algorithm was designed for the mmWave radar and camera information, and the global nearest neighbor method was used for data association. Then, the effective target sequences of the mmWave radar and the camera with successful data association were weighted to output, and the output included more accurate target orientation, longitudinal speed, and category. For the unassociated sequences, they were tracked as new targets by using the extended Kalman filter algorithm and were processed and output during the effective life cycle. Lastly, an experimental platform based on a tractor was built to verify the effectiveness of the proposed association detection method. The obstacle detection test was conducted under the ROS environment after solving the external parameters of the mmWave radar and the internal and external parameters of the camera. The test results show that the correct detection rate of obstacles reaches 86.18%, which is higher than that of a single camera with 62.47%. Furthermore, through the contrast experiment of the sensor fusion algorithms, the detection accuracy of the decision level fusion algorithm was 95.19%, which was higher than 4.38% and 6.63% compared with feature level and data level fusion, respectively. Full article

(This article belongs to the Special Issue Smart Agriculture in Nanjing Agricultural University: Celebrating 120th Anniversary)

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20 pages, 4446 KiB

Open AccessArticle

Human Grasp Mechanism Understanding, Human-Inspired Grasp Control and Robotic Grasping Planning for Agricultural Robots

by Wei Zheng, Ning Guo, Baohua Zhang, Jun Zhou, Guangzhao Tian and Yingjun Xiong

Sensors 2022, 22(14), 5240; https://doi.org/10.3390/s22145240 - 13 Jul 2022

Cited by 4 | Viewed by 3063

Abstract

As the end execution tool of agricultural robots, the manipulator directly determines whether the grasping task can be successfully completed. The human hand can adapt to various objects and achieve stable grasping, which is the highest goal for manipulator design and development. Thus, this study combines a multi-sensor fusion tactile glove to simulate manual grasping, explores the mechanism and characteristics of the human hand, and formulates rational grasping plans. According to the shape and size of fruits and vegetables, the grasping gesture library is summarized to facilitate the matching of optimal grasping gestures. By analyzing inter-finger curvature correlations and inter-joint pressure correlations, we investigated the synergistic motion characteristics of the human hand. In addition, the force data were processed by the wavelet transform algorithms and then the thresholds for sliding detection were set to ensure robust grasping. The acceleration law under the interaction with the external environment during grasping was also discussed, including stable movement, accidental collision, and placement of the target position. Finally, according to the analysis and summary of the manual gripping mechanism, the corresponding pre-gripping planning was designed to provide theoretical guidance and ideas for the gripping of robots. Full article

(This article belongs to the Special Issue Smart Agriculture in Nanjing Agricultural University: Celebrating 120th Anniversary)

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18 pages, 5694 KiB

Open AccessArticle

Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine

by Xiuguo Zou, Chenyang Wang, Manman Luo, Qiaomu Ren, Yingying Liu, Shikai Zhang, Yungang Bai, Jiawei Meng, Wentian Zhang and Steven W. Su

Sensors 2022, 22(8), 2997; https://doi.org/10.3390/s22082997 - 14 Apr 2022

Cited by 12 | Viewed by 2774

Abstract

Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage. Full article

(This article belongs to the Special Issue Smart Agriculture in Nanjing Agricultural University: Celebrating 120th Anniversary)

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16 pages, 37358 KiB

Open AccessArticle

Detection of Farmland Obstacles Based on an Improved YOLOv5s Algorithm by Using CIoU and Anchor Box Scale Clustering

by Jinlin Xue, Feng Cheng, Yuqing Li, Yue Song and Tingting Mao

Sensors 2022, 22(5), 1790; https://doi.org/10.3390/s22051790 - 24 Feb 2022

Cited by 19 | Viewed by 2526

Abstract

It is necessary to detect multi-type farmland obstacles in real time and accurately for unmanned agricultural vehicles. An improved YOLOv5s algorithm based on the K-Means clustering algorithm and CIoU Loss function was proposed to improve detection precision and speed up real-time detection. The K-Means clustering algorithm was used in order to generate anchor box scales to accelerate the convergence speed of model training. The CIoU Loss function, combining the three geometric measures of overlap area, center distance and aspect ratio, was adopted to reduce the occurrence of missed and false detection and improve detection precision. The experimental results showed that the inference time of a single image was reduced by 75% with the improved YOLOv5s algorithm; compared with that of the Faster R-CNN algorithm, real-time performance was effectively improved. Furthermore, the mAP value of the improved algorithm was increased by 5.80% compared with that of the original YOLOv5s, which indicates that using the CIoU Loss function had an obvious effect on reducing the missed detection and false detection of the original YOLOv5s. Moreover, the detection of small target obstacles of the improved algorithm was better than that of the Faster R-CNN. Full article

(This article belongs to the Special Issue Smart Agriculture in Nanjing Agricultural University: Celebrating 120th Anniversary)

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Review

Jump to: Research

25 pages, 2199 KiB

Open AccessReview

Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things

by Xiuguo Zou, Wenchao Liu, Zhiqiang Huo, Sunyuan Wang, Zhilong Chen, Chengrui Xin, Yungang Bai, Zhenyu Liang, Yan Gong, Yan Qian and Lei Shu

Sensors 2023, 23(5), 2528; https://doi.org/10.3390/s23052528 - 24 Feb 2023

Cited by 10 | Viewed by 3013

Abstract

Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures. Full article

(This article belongs to the Special Issue Smart Agriculture in Nanjing Agricultural University: Celebrating 120th Anniversary)

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